McLaren Applied Technologies

MIDAS - McLaren Integrated Data Analysis & Simulation

MIDAS - McLaren Integrated Data Analysis & Simulation

As data proliferates, its usefulness depends on our power to understand it. MIDAS is a powerful support tool that enables engineers to make decisions based on evidence rather than intuition.

McLaren developed the MIDAS (McLaren Integrated Data Analysis and Simulation) software suite because the core philosophy of its Formula 1 racing programme is that design should be data-driven. Decisions based on quantifiable simulation evidence and a consistency of approach have a higher probability of achieving optimal outcomes. Formula 1 is, on a fundamental level, a high-speed science experiment: configure-run-analyse-decide.

But decisions cannot be taken in isolation. To fully understand the consequences, we often have to revisit our decisions and interrogate the original data. Given the volume of data generated by our simulators as well as the hundreds of sensors deployed on our cars while active in the field, over time we accumulate a vast wealth of performance information. MIDAS enables us to organise this and be disciplined in the way we use it, avoiding human error.

Together with our ATLAS software, MIDAS offers state-of-the-art capabilities in simulation definition, execution and analysis. We can link simulator data to track data, compare test runs, understand trends and eliminate variables. MIDAS enables us to be organised and disciplined in our interactions with performance data, examine many different aspects of a problem, and rapidly gain insights that can lead to creative solutions. We can then test the results of that solution in the real world, analyse its effectiveness in comparison with the simulation, and feed that intelligence back into the learning loop.

Its maturity in the automotive space makes it particularly useful for car makers looking to employ tools such as the MTS Vehicle Dynamics Simulator speed products to market. We have proved its cutting-edge development capabilities by creating efficiencies in McLaren Automotive’s design cycles: no other low-volume sportscar manufacturer can claim to have launched at least one new model a year since 2010.

Data acquisition is just part of the big picture: to understand that picture, to make every new piece of data contribute to the journey and to see where historic data relates to that, you need a tool like MIDAS.

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